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Abstract:

A method to control a vehicle includes assigning a predicted driving
pattern to a predicted path for the vehicle, and providing a range for
the vehicle using the predicted energy efficiency and an amount of energy
available to the vehicle. The predicted driving pattern has an associated
predicted energy efficiency. A vehicle includes a propulsion device
coupled to wheels of the vehicle via a transmission, and a controller
electronically coupled to the propulsion device. The controller is
configured to: (i) assign a predicted driving pattern to a predicted path
for the vehicle, the predicted driving pattern having a predicted energy
efficiency, and (ii) provide a range for the vehicle using the predicted
energy efficiency and an amount of energy available to the vehicle.

Claims:

1. A method to control a vehicle comprising: assigning a predicted
driving pattern to a predicted path for the vehicle, the predicted
driving pattern having an associated predicted energy efficiency; and
providing a range for the vehicle using the predicted energy efficiency
and an amount of energy available to the vehicle.

2. The method of claim 1 wherein the predicted path and the predicted
driving pattern are based on future route information.

3. The method of claim 1 wherein the range is calculated to provide a
distance to empty for the vehicle when the vehicle has insufficient
energy to reach an end of the predicted path.

4. The method of claim 1 further comprising detecting a present driving
pattern for the vehicle, the present driving pattern having an associated
present energy efficiency, wherein the present energy efficiency is used
in calculating the range.

5. The method of claim 4 further comprising assigning the present driving
pattern to be the predicted driving pattern if the predicted path is
unknown.

6. The method if claim 4 wherein the range is calculated using the
current energy efficiency after the predicted path to provide a distance
to empty if there is sufficient energy for the vehicle to reach an end of
the predicted path.

7. The method of claim 4 further comprising determining the present
driving pattern using a driving pattern identification method with a
present driving condition.

8. The method of claim 1 further comprising displaying the range to a
user of the vehicle.

9. The method of claim 1 further comprising determining the predicted
driving pattern using a driving pattern identification method.

13. The method of claim 1 wherein the predicted driving pattern is
determined using an electronic horizon.

14. The method of claim 1 further comprising using a database to
reference a driving pattern and a corresponding energy efficiency, the
database containing possible driving patterns for an operating state of
the vehicle.

15. The method of claim 1 further comprising filtering the range when the
driving pattern changes.

16. The method of claim 1 further comprising adjusting the range using a
scaling factor if an accessory load exists.

17. The method of claim 1 further comprising adjusting the range using a
scaling factor if a predetermined ambient condition exists.

18. A method to control a vehicle comprising providing a vehicle range
using an energy efficiency corresponding to a driving pattern of the
vehicle and an amount of energy available to the vehicle, the driving
pattern determined using a driving pattern identification method.

20. A vehicle comprising: a propulsion device coupled to wheels of the
vehicle via a transmission; and a controller electronically coupled to
the propulsion device wherein the controller is configured to: (i) assign
a predicted driving pattern to a predicted path for the vehicle, the
predicted driving pattern having a predicted energy efficiency, and (ii)
provide a range for the vehicle using the predicted energy efficiency and
an amount of energy available to the vehicle.

Description:

TECHNICAL FIELD

[0001] The disclosure relates to a method of control to determine or
estimate a vehicle range for a vehicle.

BACKGROUND

[0002] Vehicles contain a certain amount of energy, in the form of
chemical fuel, electrical power, or the like, which allows them to travel
a certain distance, and may need to be refilled periodically. The
distance that a vehicle can travel using on-board energy is referred to
as the vehicle range. The projected vehicle range provides information
for a user for trip planning, minimizing driving cost, evaluating vehicle
performance and performing maintenance. The feasible range from the
remaining energy in a motor vehicle is normally referred to as Distance
to Empty (DTE), which is tied to the energy conversion efficiency of the
vehicle.

[0003] A DTE or the vehicle range may be provided for any type of vehicle
including conventional vehicles, electric vehicles, hybrid vehicles,
plug-in hybrid vehicles, fuel cell vehicles, pneumatic vehicles, and the
like.

SUMMARY

[0004] In one embodiment, a method to control a vehicle assigns a
predicted driving pattern to a predicted path for the vehicle. The
predicted driving pattern has an associated predicted energy efficiency.
The method also provides a range for the vehicle using the predicted
energy efficiency and an amount of energy available to the vehicle.

[0005] In another embodiment, a method to control a vehicle provides a
vehicle range using an energy efficiency corresponding to a driving
pattern of the vehicle and an amount of energy available to the vehicle.
The driving pattern is determined using a driving pattern identification
method.

[0006] In yet another embodiment, a vehicle is provided with a propulsion
device coupled to wheels of the vehicle via a transmission, and a
controller electronically coupled to the propulsion device. The
controller is configured to: (i) assign a predicted driving pattern to a
predicted path for the vehicle, the predicted driving pattern having a
predicted energy efficiency, and (ii) provide a range for the vehicle
using the predicted energy efficiency and an amount of energy available
to the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 is a schematic representation of a hybrid electric vehicle
powertrain capable of embodying the invention;

[0008] FIG. 2 is a diagram of the power flow paths for the components of
the powertrain shown in FIG. 1;

[0009] FIG. 3 is an overview schematic of a method to estimate vehicle
range;

[0010] FIGS. 4A and 4B are a schematic of a method to estimate vehicle
range;

[0011] FIG. 5 is a schematic of a method for providing energy
efficiencies;

[0012] FIG. 6 is a schematic of a method of calculating distance to empty;

[0013] FIG. 7 is a plot of vehicle range estimation when future driving
information is unknown;

[0014] FIG. 8 is a plot of vehicle range estimation when future driving
information is known; and

[0015] FIG. 9 is another plot of vehicle range estimation when future
driving information is known.

DETAILED DESCRIPTION

[0016] As required, detailed embodiments of the present invention are
disclosed herein; however, it is to be understood that the disclosed
embodiments are merely exemplary of the invention that may be embodied in
various and alternative forms. The figures are not necessarily to scale;
some features may be exaggerated or minimized to show details of
particular components. Therefore, specific structural and functional
details disclosed herein are not to be interpreted as limiting, but
merely as a representative basis for teaching one skilled in the art to
variously employ the present invention.

[0017] Providing an accurate DTE for a vehicle may be difficult because
vehicle range projection is connected to future driving uncertainties or
unanticipated environmental conditions. In order to calculate a
theoretical DTE for the vehicle, knowledge of the future vehicle cycles
(speed profile and road conditions) is needed because the vehicle energy
conversion efficiency is dynamically dependent on the operating
conditions which are dominated by driving cycles. Although it is
desirable to acquire the accurate speed profile and road conditions of
the scheduled vehicle journeys, it is unfeasible, and so the range needs
to be estimated using a pattern prediction method to provide a DTE for a
vehicle.

[0018] A Hybrid Electric Vehicle (HEV) structure is used in the figures
and to describe the various embodiments below; however, it is
contemplated that the various embodiments may be used with vehicles
having other propulsion devices or combinations of propulsion devices as
is known in the art. Hybrid Electric Vehicles (HEVs) typically have power
supplied by a battery powered electric motor, an engine, or a combination
thereof. Some HEVs have a plug-in feature which allows the battery to be
connected to an external power source for recharging, and are called
Plug-in HEVs (PHEVs). Electric-only mode (EV mode) in HEVs and PHEVs
allows the vehicle to operate using the electric motor alone, while not
using the engine. Operation in EV mode may enhance the ride comfort by
providing lower noise and better driveability of the vehicle, e.g.,
smoother electric operation, lower noise, vibration, and harshness (NVH),
and faster vehicle response. Operation in EV mode also benefits the
environment with zero emissions from the vehicle during this period of
operation.

[0019] Vehicles may have two or more propulsion devices, such as a first
propulsion device and a second propulsion device. For example, the
vehicle may have an engine and an electric motor, a fuel cell and an
electric motor, or other combinations of propulsion devices as are known
in the art. The engine may be a compression or spark ignition internal
combustion engine, or an external combustion engine, and the use of
various fuels is contemplated. In one example, the vehicle is a hybrid
vehicle (HEV), and additionally may have the ability to connect to an
external electric grid, such as in a plug-in electric hybrid vehicle
(PHEV).

[0020] A plug-in Hybrid Electric Vehicle (PHEV) involves an extension of
existing Hybrid Electric Vehicle (HEV) technology, in which an internal
combustion engine is supplemented by an electric battery pack and at
least one electric machine to further gain increased mileage and reduced
vehicle emissions. A PHEV uses a larger capacity battery pack than a
standard hybrid vehicle, and it adds a capability to recharge the battery
from an electric power grid, which supplies energy to an electrical
outlet at a charging station. This further improves the overall vehicle
system operating efficiency in an electric driving mode and in a
hydrocarbon/electric blended driving mode.

[0021] Conventional HEVs buffer fuel energy and recover kinematic energy
in electric form to achieve the overall vehicle system operating
efficiency. Hydrocarbon fuel is the principal energy source. For PHEVs,
an additional source of energy is the amount of electric energy stored in
the battery from the grid after each battery charge event.

[0022] While most conventional HEVs are operated to maintain the battery
state of charge (SOC) around a constant level, PHEVs use as much
pre-saved battery electric (grid) energy as possible before the next
battery charge event. The relatively low cost grid supplied electric
energy is expected to be fully utilized for propulsion and other vehicle
functions after each charge. After the battery SOC decreases to a low
conservative level during a charge depleting event, the PHEV resumes
operation as a conventional HEV in a so-called charge sustaining mode
until the battery is re-charged.

[0023] FIG. 1 illustrates an HEV 10 powertrain configuration and control
system. A power split hybrid electric vehicle 10 may be a parallel hybrid
electric vehicle. The HEV configuration as shown is for example purposes
only and is not intended to be limiting as the present disclosure applies
to vehicles of any suitable architecture, including HEVs and PHEVs.

[0024] In this powertrain configuration, there are two power sources 12,
14 that are connected to the driveline: 12) a combination of engine and
generator subsystems using a planetary gear set to connect to each other,
and 14) the electric drive system (motor, generator, and battery
subsystems). The battery subsystem is an energy storage system for the
generator and the motor.

[0025] The changing generator speed will vary the engine output power
split between an electrical path and a mechanical path. In addition, the
control of engine speed results in a generator torque to react against
the engine output torque. It is this generator reaction torque that
conveys the engine output torque to the ring gear of the planetary gear
set 22, and eventually to the wheels 24. This mode of operation is called
"positive split". It is noted that because of the kinematic properties of
the planetary gear set 22, the generator 18 can possibly rotate in the
same direction of its torque that reacts against the engine output
torque. In this instance, the generator 18 inputs power (like the engine)
to the planetary gear set to drive the vehicle 10. This operation mode is
called "negative split".

[0026] As in the case of the positive split mode, the generator torque
resulting from the generator speed control during a negative split reacts
to the engine output torque and conveys the engine output torque to the
wheels 24. This combination of the generator 18, the motor 20 and the
planetary gear set 22 is analogous to an electro-mechanical CVT. When the
generator brake (shown in FIG. 1) is actuated (parallel mode operation),
the sun gear is locked from rotating and the generator braking torque
provides reaction torque to the engine output torque. In this mode of
operation, all the engine output power is transmitted, with a fixed gear
ratio, to the drivetrain through the mechanical path.

[0027] In a vehicle 10 with a power split powertrain system, unlike
conventional vehicles, the engine 16 requires either the generator torque
resulting from engine speed control or the generator brake torque to
transmit its output power through both the electrical and mechanical
paths (split modes) or through the all-mechanical path (parallel mode) to
the drivetrain for forward motion.

[0028] During operation using the second power source 14, the electric
motor 20 draws power from the battery 26 and provides propulsion
independently of the engine 16 for forward and reverse motions. This
operating mode is called "electric drive" or electric-only mode or EV
mode. In addition, the generator 18 can draw power from the battery 26
and drive against a one-way clutch coupling on the engine output shaft to
propel the vehicle 10 forward. The generator 18 alone can propel the
vehicle 10 forward when necessary. This mode of operation is called
generator drive mode.

[0029] The operation of this power split powertrain system, unlike
conventional powertrain systems, integrates the two power sources 12, 14
to work together seamlessly to meet the driver's demand without exceeding
the system's limits (such as battery limits) while optimizing the total
powertrain system efficiency and performance. Coordination control
between the two power sources is needed. As shown in FIG. 1, there is a
hierarchical vehicle system controller (VSC) 28 that performs the
coordination control in this power split powertrain system. Under normal
powertrain conditions (no subsystems/components faulted), the VSC
interprets the driver's demands (e.g. PRND and acceleration or
deceleration demand), and then determines the wheel torque command based
on the driver demand and powertrain limits. In addition, the VSC 28
determines when and how much torque each power source needs to provide in
order to meet the driver's torque demand and to achieve the operating
point (torque and speed) of the engine.

[0030] The battery 26 is additionally rechargeable in a PHEV vehicle 10
configuration (shown in phantom), using a receptacle 32 which is
connected to the power grid or other outside electrical power source and
is coupled to battery 26, possibly through a battery charger/converter
30.

[0031] The vehicle 10 may be operated in electric mode (EV mode), where
the battery 26 provides all of the power to the electric motor 20 to
operate the vehicle 10. In addition to the benefit of saving fuel,
operation in EV mode may enhance the ride comfort through lower noise and
better driveability, e.g., smoother electric operation, lower noise,
vibration, and harshness (NVH), and faster response. Operation in EV mode
also benefits the environment with zero emissions from the vehicle during
this mode.

[0032] A method for use with the vehicle 10 uses pattern prediction from a
driving pattern identification method and off-board simulations (or
vehicle tests) to provide a DTE estimation for the vehicle. The driving
pattern identification method uses an algorithm that detects and
recognizes real-world driving conditions as one of a set of standard
drive patterns, including for example, city, highway, urban, traffic, low
emissions, etc. In one embodiment, the algorithm is based on machine
learning using a neural network. In other embodiments, the algorithm is
based on support vector machines, fuzzy logic, or the like.

[0033] Regarding the driving pattern identification method, it is known
that fuel efficiency is connected to individual driving styles, roadway
types, and traffic congestion levels. A set of standard drive patterns,
called facility-specific cycles, have been developed to represent
passenger car and light truck operations over a broad range of facilities
and congestion levels in urban areas. (See, for instance, Sierra
Research, 30 `SCF Improvement--Cycle Development`, Sierra Report No.
SR2003-06-02 (2003).) Driving styles have been captured in these standard
drive patterns as well. For example, for the same roadway type and
traffic level, different drivers may lead to different drive patterns. An
online driving pattern identification method that automatically detects
real-world driving condition and driving style and recognizes it as one
of the standard patterns has been developed. (See, for example, Jungme
Park, ZhiHang Chen, Leonidas Kiliaris, Ming Kuang, Abul Masrur, Anthony
Phillips, Yi L. Murphey, `Intelligent Vehicle Power Control based on
Machine Learning of Optimal Control Parameters and Prediction of Road
Type and Traffic Congestions`, IEEE Transactions on Vehicular Technology,
17 Jul. 2009, Volume 58, Issue 9.) This online driving pattern method is
based on machine learning using a neural network and its accuracy has
been proven by simulations.

[0034] The driving pattern identification method chooses sequences of
`drive pattern` as the most effective high-level representation of the
traffic speed, road condition and driving style, as the basis to
calculate the average energy efficiency for DTE calculation. By
sequencing drive patterns for a future vehicle route, trip or path, the
cost and uncertainties of acquiring the precise future speed profiles and
road conditions. The path, trip, or route may be entered or indicated by
a user, or may be provided using an electronic horizon, which computes a
route probability based on roads near the vehicle, the direction or the
vehicle, etc. For example, if a vehicle is on a highway, the electronic
horizon will use a highway path and the distance to the next exit as
future predicted information, and then switch to an unknown, unpredicted
future.

[0035] In order to provide a DTE for the vehicle, the VSC 28 uses a
driving pattern and driving style identification method and vehicle
simulation models. The driving pattern and driving style identification
method, such as described in co-pending U.S. patent application Ser. No.
13/160,907, entitled "A Method to Prioritize Electric-Only Operation (EV)
for a Vehicle," filed on Jun. 15, 2011, the disclosure of which is
incorporated in its entirety by reference herein. The driving style and
identification method automatically detects and recognizes real-world
driving condition or driving aggressiveness as one of the standard
patterns or driving styles.

[0036] High-fidelity vehicle simulation models represent the actual
vehicle with built-in controllers. The simulation can compute the Vehicle
Energy Efficiency (`MPG`/`Miles per Gallon` for fueled vehicles or `Miles
per kWh` for electrical vehicles) under any driving pattern represented
by typical driving cycles. The simulation results typically match or
correlate to the actual vehicle test results.

[0037] FIG. 3 illustrates a simplified schematic for the method of
calculating a DTE or a vehicle range. Taking into consideration both
predicted future and current driving patterns, the algorithm performs a
calculation 38 with data fed from three main paths to estimate or provide
a DTE for the vehicle. An off-board computation 40 of the `energy
efficiency lookup tables` is done in advance and loaded into the VSC 28
as a lookup table, or the like. Any future information available is
determined at 42 and used in an on-board computation 44 to provide the
average energy efficiency for the `predicted future driving patterns`
determined using a driving pattern identification method. Historical and
current driving information is determined at 46 and provided to on-board
computations 48 of the average energy efficiency for the `current driving
pattern`, which is determined using a driving pattern identification
method.

[0038] FIGS. 4A and 4B depict a more detailed schematic of the method of
estimating and providing a DTE for the vehicle. Offline tests or
simulations 50 provide energy efficiency lookup tables 52 which provide a
driving pattern and an associated energy efficiency for each pattern. The
tables are created offline, however, it is also contemplated that the
tables could be created or updated while the vehicle operates, or
on-line.

[0039] Future diving patterns and efficiencies are determined through
sequence 54. Predicted speeds, road conditions, and/or traffic
information 56 is provided by a navigation system, cellular network,
and/or vehicle to vehicle network 58. A traffic model 60 may be present
which provides additional predicted traffic considerations into the
sequence 54. The predicted speeds of the vehicle and the other road and
traffic conditions are provided to a pattern parameter extraction
function 62, which in turn provides pattern parameters 64 to a pattern
recognition function 66. The pattern recognition function 66 provides a
predicted future driving pattern 68 for use in sequence 54.

[0040] An energy efficiency calculation 70 uses one or more predicted
future driving patterns 68, the energy efficiency tables 52, and any data
72 available regarding the vehicle with respect to vehicle weight, tire
pressure and the like which may affect efficiency. The calculation 70
then provides an average energy efficiency for the predicted patterns 74.

[0041] A sequence 76 is also provided to determine the present driving
pattern and efficiency. The VSC 28 uses various vehicle sensors, inputs
to a CAN bus, and the like at 78 and signal processes them at 80 to
provide processed information 82 such as vehicle speeds, road grade, etc.

[0042] The processed information 82 is provided to a pattern parameter
extraction function 84, which in turn provides pattern parameters 86 to a
pattern recognition function 88. The pattern recognition function 88
provides a present or current driving pattern 90 for use in sequence 76.

[0043] An energy efficiency calculation 92 uses the current driving
patterns 90, the energy efficiency tables 52, and any data 72 available
regarding the vehicle with respect to vehicle weight, tire pressure and
the like which may affect efficiency. The calculation 92 then provides an
average energy efficiency for the current driving pattern 94.

[0044] A load modifier 96 uses the average efficiency of the current
pattern 94 and any random load information 98 to provide an adjusted
average efficiency of the current pattern 100. A random load may be
weather conditions, the environmental state, an ambient condition, and/or
a vehicle accessory is in use, such as an HVAC system. A random load
modifier may also be present in sequence 54 (not shown) using weather
forecasts and the like to adjust the predicted future energy efficiency.

[0045] The various inputs are arbitrated at 102 to calculate a raw range
estimation 104. The arbitration considers the predicted future driving
patterns 68, the average efficiency of the predicted future driving
patterns 74, the average efficiency of the current driving pattern 100,
an estimated distance of the predicted driving zone, path, or route 106,
and the remaining energy 108 available to the vehicle.

[0046] The raw range estimate 104 may be modified at 110 for various
driving styles 112. The driving style 112 is determined during sequence
76. The processed information 82 is provided to a pattern parameter
extraction function 114, which provides pattern parameters to determine
the driving style at 116 based on the current vehicle driving data.

[0047] Filtering of range occurs at 118. The filtering acts to remove
hysteresis in the range and provides a smoothed fuel economy number and
improves user perception. The final estimated DTE or range may then be
provided to the user at 120 via a screen, human-machine interface (HMI),
gauge, or the like.

[0048] Referring now to FIG. 5, an off-board method to calculate a fuel
economy table 50 is provided. The step 50 calculates and stores the
average vehicle energy efficiency for each driving pattern by performing
a model simulation or running an actual vehicle test. For example, the
vehicle energy efficiency for driving Patternk may be obtained by
either: Effk=SimFE(Model, Patternk) or
Effk=TESTFE(Vehicle, Patternk). The units of the `vehicle
energy efficiency` may be chosen as `distance/volume` since people
normally use `MPG` or `MPkWh` to indicate the vehicle energy efficiency.

[0049] The step 50 cycles through the range of potential driving patterns
during the test or simulations phase at 122 to calculate an efficiency
for each pattern. A table or correlation is then provided at 124 which
includes the potential vehicle driving patterns and as associated energy
efficiency for each.

[0050] The above simulations or vehicle tests 50 can be augmented by
considering additional factors such as different vehicle weight, tire
pressure, etc. These parameters may be used as additional inputs of the
energy efficiency look-up tables. For example, an even more accurate
vehicle energy efficiency for driving Patternk may be obtained by
either: Effk=SimFE(Model, Patternk, Tire Pressure, Vehicle
Weight, . . . ) or Effk=TESTFE(Vehicle, Patternk, Tire
Pressure, Vehicle Weight, . . . ).

[0051] The energy efficiency numbers generated above for the table 124 are
needed for the on-board DTE calculation. The average vehicle energy
efficiency should be consistent when simulated in the same drive pattern,
but it varies with different driving patterns so that the DTE prediction
can be updated upon changing current and future driving conditions to fit
the customer's perception. Step 50 performs NumPattern (i.e., the total
number of driving patterns) of iterations during 122 and the results are
stored in CAL table 124 to be used on-board.

[0052] The pattern parameter extraction functions, shown as 62, 84 and 114
in FIG. 4, each represent a process to collect available pattern
parameters, or to convert available information into typical driving
pattern parameters. The function 62 extracts pattern parameters for
predicting future driving patterns. Function 84 extracts pattern
parameters for predicting the current driving pattern. Function 114
extracts pattern parameters for predicting a current driving style.
Typical pattern parameters include: total distance of driving, average
speed, maximum speed, standard deviation (SD) of acceleration, average
acceleration, maximum acceleration, average deceleration, maximum
deceleration, percentage of time within a specified speed interval, and
percentage of time within a specified deceleration interval. Other
parameters are also contemplated.

[0053] The parameters affect fuel usage and may be used to differentiate
between driving patterns, and may be observed, calculated or approximated
from multiple information sources. For example, most pattern parameters
for the `current` driving condition are extracted from the most-recent
speed profile recorded on-board by the VSC 28, and processed into the
desired format. Additionally with the availability of navigation systems,
V2V/V2I (Vehicle to Vehicle/Vehicle to Infrastructure) and cellular/other
networks, and traffic modeling, future information can be collected and
processed into typical pattern parameters at 62.

[0054] Steps 70 and 92 lookup the corresponding average energy efficiency
for the predicted driving patterns and current driving pattern,
respectively. For example, if Patternk is recognized as the current
driving pattern by 92, the `Average Vehicle Energy Efficiency` of
Patternk may be looked up as:
Eff_Averagek=Average_Eff_Table(Patternk, Tire Pressure, Vehicle
Weight . . . ).

[0055] Similarly if the future patterns are recognized as Patternt,
Patternt+i, . . . Patternt+Tend, step 70 lookups a set of
`Average Vehicle Energy Efficiency` numbers that correspond to the
predicted patterns, where t is the time. Tend may be either the end
of a trip or known future information, or may refer to partway through a
trip.

[0056] The range or DTE arbitration and calculation 102 is depicted in
greater detail in FIG. 6. The algorithm determines if predicted future
patterns are available at 130. If predicted patterns are not available,
the algorithm goes to step 132 and calculates the DTE using the current
driving pattern energy efficiency and the amount of energy available to
the vehicle.

[0057] A scenario for step 132 is depicted in FIG. 7. If no future
information is available or can be acquired, the future driving patterns
are assumed to be the same as the `current driving pattern`, which is
updated continuously as the on-board recognition algorithm collects the
most recent driving data within a moving window. Alternatively, step 132
may assume another representative pattern explored from an individual
driver's historical data. Once the assumed current driving pattern (e.g.
Patternk) is determined, step 132 calculates `Distance to Empty`
assuming that Patternk sustains until the vehicle has run out of
energy using DTEt=(Remaining Energy)*Eff_Averagek.

[0058] If predicted patterns are available, the algorithm goes to step 134
to calculate the total energy needed for the predicted zone(s) using the
expected distance for each future driving pattern and the energy
efficiency for that pattern as shown in FIG. 6. Once the total predicted
energy needed has been calculated at 134, the algorithm calculates the
amount of energy remaining at 136. The amount of energy remaining at 136
uses the time to empty, or the time that all of the energy available to
the vehicle has been depleted such that the remaining energy is zero or
another set floor value.

[0059] The algorithm 102 then compares the amount of energy needed to the
amount of energy remaining at 138. If the amount of energy remaining is
greater than the amount of energy needed, the algorithm proceeds to step
140. If the amount of energy remaining is less than then amount of energy
needed, the algorithm proceeds to step 142.

[0060] A scenario for step 142 is depicted in FIG. 8. The total energy
needed is calculated for the distance or length of the prediction zone
as:

[0062] and for this scenario, the time to empty, Tempty, occurs
before Tend, the time to the end of the prediction zone.

[0063] The distance to empty (DTE) is then solved for by the algorithm by
integrating the distances of the known patterns from the current time to
the time to empty as:

DTE t = t = T current T empty Distance t
t ##EQU00003##

[0064] and this DTE may be provided to the user.

[0065] A scenario for step 142 is depicted in FIG. 9. Here, the future
driving pattern is predicted from known future driving information, and
the on-board energy (or remaining energy) is greater than the energy
needed such that the vehicle can cover more than the entire distance of
the prediction zone with the energy on-board. The patterns and the energy
efficiencies are predicted within the prediction zone shown in FIG. 9.
The driving pattern beyond the prediction zone is unknown, however, there
is still energy available to the vehicle in this scenario.

[0066] The algorithm assumes the driving pattern beyond Tend to be
the same as the `current driving pattern` in order to calculate a DTE for
the vehicle. For example, if the unknown future may be assumed to be
Patternk, where Eff_Averagek=Average_Eff_Table (Patternk, Tire
Pressure, Vehicle Weight . . . ), then the DTE for the scenario as shown
in FIG. 9 may be calculated as:

[0068] Referring back to FIG. 4, modifier 96 adjusts the average energy
efficiency of `current driving pattern` by considering `random loads`
such as heating, ventilation, and air-conditioning (HVAC) use, stereo,
other accessory use, weather, and other environmental states. The
adjustments are done through a set of scaling factors.

[0069] For example, auxiliary loads increase energy consumption for a
given driving pattern. The impact of the loads is drive-cycle dependent,
so by estimating the impact of the loads on energy/fuel usage for each of
the driving patterns, the impact on overall energy consumption may be
estimated. The energy-impact of the auxiliary loads, such as belt-driven
air conditioning, electrical loads, etc., can be estimated. Given a set
of operating conditions such as environmental temperature, humidity, sun
load, etc., the DTE algorithm may statistically estimate the probable
auxiliary loads and modify the energy consumption accordingly by using
look-up tables containing the relationships between auxiliary loads and
energy consumption. Other factors such as an individual user's auxiliary
load preferences taken from historical data (e.g., climate control and/or
daytime driving lights) can also be used to calibrate the modifier 96.

[0070] The modifier 110 may also consider an individual's driving style
112 which impacts the range estimation for DTE. Based on the
self-learning result of driving style in 116, a weighting factor may be
applied in modifier 110 to adjust the raw estimation 104. Average
efficiency of both the `predicted patterns` and `current driving pattern`
may be modified by 110 because driving style is a characteristic of the
user.

[0071] The scaling or weighting factors in modifier 96 and 110 are stored
as calibrations that are tuned to match vehicle tests and model
simulations.

[0072] Filtering 118 filters the `Distance to Empty` for the display
continuity to provide a final range estimation 120. The filtering
function 118 smoothes out discontinuities of the DTE readout as the
vehicle switches between roadway types. If no pattern change is detected
the filtering remains inactive.

[0073] The method of calculating a DTE is applicable to all types of
vehicles, including hybrid and battery electric vehicles. The method
establishes vehicle energy efficiency by taking into account real-world
driving conditions and driver styles from historical and predicted
driving data.

[0074] Various input variables for the on-board calculation of DTE may be
accessible through vehicle gauges, an on-board diagnostic interface,
sensors, and the like and include: remaining energy, distance traveled,
and average energy efficiency for the vehicle. A readout provides the DTE
to a user.

[0075] It also should be noted that some inputs for the algorithm as shown
in FIG. 4 are easy to measure or already exist for use by the VSC 28 in
the vehicle. For example, `Distance Traveled` may be calculated by taking
the last distance reading and adding the incremental distance (calculated
by multiplying the current speed with the time interval between
readings). `Remaining Energy` may be reported by the battery module or
fuel gauge. In the case of multiple energy sources, the VSC 28 may
calculate the total `equivalent energy` for the DTE algorithm.

[0076] The methods and algorithms are independent of any particular
programming language, operating system processor, or circuitry used to
develop and/or implement the control logic illustrated. Likewise,
depending upon the particular programming language and processing
strategy, various functions may be performed in the sequence illustrated
at substantially the same time or in a different sequence. The
illustrated functions may be modified or in some cases omitted without
departing from the spirit or scope of the present invention.

[0077] While exemplary embodiments are described above, it is not intended
that these embodiments describe all possible forms of the invention.
Rather, the words used in the specification are words of description
rather than limitation, and it is understood that various changes may be
made without departing from the spirit and scope of the invention.
Additionally, the features of various implementing embodiments may be
combined to form further embodiments of the invention.